🔎 Interpret Your Correlation Matrix (Pearson’s r) Instructions:

p < .05, p < .01*

From the correlation matrix, several relationships of different strengths can be identified.

First, there is a strong positive correlation between study time and GPA (r = .62, p < .01). This indicates that students who spend more time studying tend to achieve higher GPAs. In simple terms, increased study time is strongly associated with better academic performance.

Second, a moderate positive correlation is found between GPA and writing confidence (r = .41, p < .05). This suggests that students with higher GPAs tend to feel more confident in their writing abilities. While the relationship is not extremely strong, it still reflects a meaningful connection between academic success and self-perception.

Third, a weak positive correlation appears between AI use frequency and writing confidence (r = .12, p > .05). This implies that students who use AI tools more frequently may feel slightly more confident in their writing, but the relationship is weak and not statistically significant. Therefore, this finding should be interpreted with caution.

One somewhat surprising result is the non-significant relationship between AI use frequency and GPA (r = .28, p > .05). Although the correlation is positive, it is not strong enough to be statistically significant, suggesting that using AI tools does not necessarily translate into higher academic performance.

Overall, the matrix shows that study time has the strongest relationship with GPA, while other variables demonstrate moderate to weak associations. This highlights that traditional factors like study habits may still play a more important role than technological support in academic outcomes.